# Machine-learning-assisted correction of correlated qubit errors in a topological code

## TL;DR

This paper demonstrates that a recurrent neural network can be trained with experimental data to improve error detection in the surface code, outperforming traditional decoders by capturing error correlations without needing a noise model.

## Contribution

It introduces a machine learning-based decoder for the surface code that adapts to physical systems and maintains performance over many cycles, surpassing existing methods.

## Key findings

- Neural network decoder outperforms minimum-weight perfect matching decoder.
- The decoder detects correlations between X and Z errors.
- Performance remains stable over multiple error correction cycles.

## Abstract

A fault-tolerant quantum computation requires an efficient means to detect and correct errors that accumulate in encoded quantum information. In the context of machine learning, neural networks are a promising new approach to quantum error correction. Here we show that a recurrent neural network can be trained, using only experimentally accessible data, to detect errors in a widely used topological code, the surface code, with a performance above that of the established minimum-weight perfect matching (or blossom) decoder. The performance gain is achieved because the neural network decoder can detect correlations between bit-flip (X) and phase-flip (Z) errors. The machine learning algorithm adapts to the physical system, hence no noise model is needed. The long short-term memory layers of the recurrent neural network maintain their performance over a large number of quantum error correction cycles, making it a practical decoder for forthcoming experimental realizations of the surface code.